Tiger Analytics

Tiger Analytics

Tiger Analytics is a leading advanced analytics consulting firm partnering with Fortune 100 companies to drive business value through expertise in marketing science, customer analytics, and operations analytics.

Professional Services
1K-5K
Founded 2011

Description

  • Provide solutions for deployment, execution, validation, monitoring, and improvement of machine learning engineering solutions.
  • Create scalable machine learning systems for enterprise use cases.
  • Build reusable production data pipelines for implemented machine learning models.
  • Write production-quality code and libraries that can be packaged as containers and deployed.
  • Collaborate with cross-functional teams and business partners on solution delivery and strategy.
  • Use analytical insights to help drive business value and communicate results to stakeholders.
  • Design, develop, test, and research agentic AI architectures and systems.
  • Implement multi-agent workflows, orchestration, caching, and memory management where needed.
  • Support model evaluation, benchmarking, and A/B testing for prompt and LLM variations.

Requirements

  • 8+ years of industry experience in AI/ML and data engineering.
  • Strong proficiency in Python.
  • Hands-on expertise with agentic AI frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, AutoGen, or the OpenAI Agentic SDK.
  • Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML.
  • Hands-on experience with generative AI models, RAG architecture, and NLP.
  • Familiarity with AWS services such as SageMaker, EC2, and S3, and/or Google Cloud Platform.
  • Proficiency in data preprocessing and feature engineering.
  • Experience with GitHub, VS Code, Jupyter Notebook, and Docker.
  • Knowledge of Snowflake and Oracle is a plus.
  • Familiarity with AWS Bedrock API and/or other GenAI APIs.
  • Experience working in an Agile framework.
  • Experience designing IR/RAG systems with vector databases and knowledge graphs.
  • Strong skills in prompt engineering, including zero-shot, few-shot, and chain-of-thought approaches.
  • Experience conducting rigorous model evaluation, A/B testing, and performance benchmarking.
  • Insurance or financial domain experience is a big plus.
  • Working knowledge of Google Cloud Platform and GCP AI Platform/Vertex AI is a plus.

Benefits

  • Opportunity for significant career development in a fast-growing entrepreneurial environment.
  • High degree of individual responsibility.
  • Chance to work on challenging enterprise analytics and AI use cases.

Interested in this position?

Apply directly on the company website

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